This paper is in the following e-collection/theme issue:

Theme Issue 2020:National NLP Clinical Challenges/Open Health Natural Language Processing 2019 Challenge Selected Papers (17) Clinical Information and Decision Making (1340) Decision Support for Health Professionals (1147) Machine Learning (1401) Electronic Health Records (971) Natural Language Processing (650)

Published on in Vol 9, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22797, first published .
A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System

A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System

A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System

Authors of this article:

Youngjun Kim1 Author Orcid Image ;   Paul M Heider1 Author Orcid Image ;   Isabel RH Lally2 Author Orcid Image ;   Stéphane M Meystre1 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

Journals

  1. Landolsi M, Hlaoua L, Ben Romdhane L. Information extraction from electronic medical documents: state of the art and future research directions. Knowledge and Information Systems 2023;65(2):463 View
  2. Landolsi M, Romdhane L, Hlaoua L. Medical Named Entity Recognition using Surrounding Sequences Matching. Procedia Computer Science 2022;207:674 View
  3. Landolsi M, Hlaoua L, Romdhane L. Extracting and structuring information from the electronic medical text: state of the art and trendy directions. Multimedia Tools and Applications 2023;83(7):21229 View